A Sociological Analysis of Structural Racism in Student List Products

Ozan Jaquette

UCLA

Karina Salazar

University of Arizona

Crystal Han

Stanford

Patrica Martín

UCLA

ozanj.github.io/student_list_policy/slides/student_list_cshpe.html

— .section

Introduction


College Board Search and student outcomes

Howell, Hurwitz, Mabel, and Smith (2021)


Scholarship on college access

Extant literatures, not mutually exclusive

  • Student behavior
  • Behavior of schools and postsecondary institutions (PSIs)
    • Scholarship on enrollment management fits here
      • Scholarship on admissions fits here
  • Effects of federal, state, and local policies


Third-party providers; the other for-profit industry in education

  • Schools and PSIs outsource functions to vendors and consultancies (Jaquette, Salazar, and Martin, 2022; Komljenovic, 2021; Komljenovic, 2022)
  • Scholars have not investigated how third-party produces and services structure college access


Developing a literature about algorithmic products

  • Literature at the intersections of access, enrollment management, and edtech
  • Analyze products purchased by schools and PSIs
  • Analyze the organizations that sell these products

Structural racism in algorithmic products

Structural racism

  • Processes – often viewed as neutral or common-sense – that systematically advantage dominant groups and disadvantage marginalized groups


Critical turn in higher education research - Experiences of individual people provide insight about structural racism


Critical data studies (e.g., and Benjamin, 2019; and Noble, 2018) and sociology of race (e.g., and Cottom, 2020; and Norris, 2021) finds algorithmic reproduce/increase racial inequality - Structurally racist inputs: Seemingly neutral inputs that correlate with race because of historical exclusion from this input (e.g., zip code, AP exam scores)


RQ: What is the relationship between student list search filters (e.g., test score range, zip code) and the characteristics of students who are included vs. excluded by student list purchases? - Analyze student lists purchased from College Board - Focus on race and class inequality in which prospects are included/excluded by student list purchases

— .section

Background on student Lists

— .subsection

Lists vis-a-vis recruiting


The US market for higher education

A national voucher system - Tuition revenue: household savings; grants and loans from federal, state, and private sources - Tuition revenue follows students to Title IV institutions


Students - Goal: want to attend college - Problem: don’t know all options, where they would be admitted, how much it will cost


Universities - Goal: enroll students to survive and other enrollment goals - Problem: can’t rely solely on students who reach out on their own; don’t know the prospects or how to contact them


Student lists - A matchmaking intermediary that connects institutions to prospects - “lead generation” - Student lists are an example of list-based leads, based on direct mail - As opposed to behavioral-based leads (e.g., ads from Google Search)

— &twocol

The enrollment funnel

*** =left

Prospects

  • Population of desirable potential students

Leads

  • Prospects whose contact info has been obtained

Inquiries

  • Prospects who have contacted the institution
    • Institution as first contact (leads)
    • Student as first contact


Interventions along the funnel

  • Convert prospects to leads
    • purchase student lists
  • Convert leads/inquiries to applicants
    • Email, mail, targeted social media
  • Convert admits to enrolles
    • Financial aid packages

*** =right

The enrollment funnel


Enrollment Funnel

Source: pngwing.com


College Board and ACT student list products

Sources of student list data

  • Test takers (e.g., PSAT, SAT, AP), pre-test questionnaire (demographics, preferences)
  • More recently, from college search engines (e.g., College Board Big Future)
  • Students can opt in or out


What information does a list contain (College Board template) - Contact, demographic, college preferences, limited academic achievement


Pricing

  • Historically, a price-per-prospect model (e.g., $0.50 per name)
  • ACT and College Board moving to subscription model


Buying student lists

  • “Search filters” control which prospects included/excluded from a purchase
  • Commonly used search filters (Link to ACT filters)
    • Graduation year, HS GPA, test score range, gender, race/ethnicity, geography (e.g., zip-code)
  • New filters based on predictive analytics to facilitate micro-targeting
    • e.g., “geodemographic” filters target prospects based on past behavior of nearby students

— .subsection

EM Market dynamics


Dynamics shaping the market for student list data

From The Student List Business: Primer and market dynamics (Jaquette, Salazar, and Martin, 2022):

  1. Centrality of enrollment management (EM) consulting firms
  • Purchase student lists on behalf of universities
  • Names are input to firm predictive models and recruiting interventions
  1. Competition in the 2000s
  • Technology >> new sources of student list data (e.g., college search engines; software used by high schools) >> entry by new vendors (e.g., Zinch)
  • Note: all student list data is derived from user-data from students on platforms
  1. Concentration in the 2010s
  • Horizontal acquisitions in EM consulting industry (e.g., RuffaloCODY acquires Noel-Levitz)
  • Vertical acquisitions transform market for student list data (e.g., PowerSchool acquires Naviance/Intersect from Hobsons; EAB acquires Cappex)
    • Leverage control over pool of names to sell software-as-service products
  1. Incumbents College Board and ACT evolve amidst threat from test-optional
  • Create new search filters that aid micro-targeting of prospects
  • Leverage names database to sell EM consulting
  • Create/buy college search engines

— .section

Literature Review

— .subsection

Recruiting


Sociological scholarship on recruiting

Enrollment funnel: prospects, leads, inquires, applicants, admits, enrolled - Most scholarship focuses on latter stages (e.g., which applicants get admitted) - Growing body of research analyzes recruiting “in the wild”


Recruiting from perspective of high school students (Holland, 2019) - Underrepresented students sensitive to feeling “wanted” by colleges


Connections between (fancy) high schools and (fancy) colleges from an orgs perspective - Off-campus recruiting visits indicate a network tie and enrollment priorities - recruiting from perspective of: private college (Stevens, 2007); private HS counselors (Khan, 2011) - Recruiting visits by public research universities (e.g., Salazar, Jaquette, and Han, 2021; and Salazar, 2022)


Recruiting at open-access PSIs for adults (e.g., and Cottom, 2017; and Posecznick, 2017) - For-profits have demand in Black/Latinx communities because traditional colleges ignore them


Hook - Scholarship assumes that recruiting is something done by colleges - Ignores products and consultancies that structure recruiting

— .subsection

Sociology of race


Racialized social systems and structural racism

Most social sciences define racism as ideology held by individuals (e.g., explicit or implicit racial bias) - Measures societal racism by examining the attitudes of individuals - Excludes the possibility that institutions can be racist


Bonilla-Silva (1997): focus on underlying social structure instead of individual ideology

  • Racialized social systems: “societies that allocate differential economic, political, social, and even psychological rewards to groups along racial lines” (p. 474)
  • Racial groups are a social construction of a racialized social system
    • Institutions allocate benefits to racial groups based on socially constructed racial hierarchy
  • “Only way to ‘cure’ society of racism is by eliminating its systemic roots” (p. 476) within institutions


Structural racism

  • “systematic racial bias embedded in the ‘normal’ functions of laws and social relations” (Tiako, South, and Ray, 2021, p. 1143); processes viewed as neutral systematically advantage dominant groups.


Race is fundamental to capitalism (racial capitalism)

  • Source of profit is exploitation based on construction of race (Du Bois, 1935; Robinson, 2000)
  • Analyses can focus on production (labor) or consumer (e.g., credit, housing) side of economy

Algorithms and actuarialism

Algorithms

  • “sets of instructions written as code and run on computers” (Burrell and Fourcade, 2021, p. 215)


Algorithmic products utilize actuarial methods and logic

  • Actuarial methods proceed in two steps (Hirschman and Bosk, 2020)
    1. Model previous cases in order to identify determinants of an outcome
    2. Apply these results to future cases in order to make predictions and assign risk levels
  • Acturarialism (Simon, 1988)
    • ideology that equates fairness with risk, as determined by predicted probabilities
    • e.g., businesses that have characteristics associated with default should pay higher interest rates


Actuarialism and standardization

  • Actuarial products remove individual judgment from decision-making
  • Reduces racial inequity due to prejudice of individual decision-makers (Hirschman and Bosk, 2020)

Actuarial methods reproduce structural racism

Classification situations (Fourcade and Healy, 2013)

  • Defined as use of actuarial techniques by orgs to categorize consumers into different groups
  • Binary classifications: loans offered to consumers with “good” credit, but not bad credit
  • Advances in data analytics >> categorize customers into many groups
    • Tiered products with costs and benefits tied to level of risk (e.g., payday loan)
  • Predatory inclusion (Seamster and Charron-Chénier, 2017; Cottom, 2020)
    • Target marginalized consumers for “democratizing mobility schemes on extractive terms”


Structurally racist inputs

  • Actuarial products predict future outcome by modeling determinants using historical data
    • “Predicting the future on the basis of the past threatens to reify and reproduce existing inequalities of treatment by institutions” (Burrell and Fourcade, 2021, p. 224).
  • Structurally racist input: determinant correlated with race because people of color have been historically excluded (e.g., Obermeyer, Powers, Vogeli, and Mullainathan, 2019)
  • Structural racism in Moody’s credit rating algorithm for city governments (Norris, 2021)
    • %Black negatively associated with city rating until control for median household income
    • Median household income is a “racialized input”:
      • Seemingly neutral, structurally racist input that masks structural racism of algorithm

Micro-targeting and market segments

  • Micro-targeting: identify granular segments of society with great precision
  • Market segmentation: categorize customers into groups for advertisers (e.g., “married sophisticates”)


Racial exclusion is consequence of micro-targeting, market segmentation (Benjamin, 2019; Noble, 2018)

  • These technologies could be used to target marginalized groups, but in practice they are not
  • When developing classification systems, developer bias and structurally racist inputs enter algorithm
  • Classification systems designed for profit, do not create audience segments not valued by advertisers


Micro-targeting and segmentation by Facebook (Cotter, Medeiros, Pak, and Thorson, 2021, p. 1)

  • “Driven not by a goal of making all users available to advertisers, but of making the ‘right’ individuals”
  • Tells advertisers choose “‘targeting strategy that focuses on reach and precision and eliminates waste’”


Student list products - College Board Student Search: “create a real pipeline of best-fit prospects” - Ruffalo Noel Levitz (2021): “target the right students in the right markets” by making “the most efficient name purchases using predictive modeling”


Hook: Sociology of race has not studied products that help orgs identify customers (I think)

— .section

Conceptual Framework


Conceptual Framework

Primary research question:

  • What is the relationship between student list search filters (e.g., test score range, zip code) and the characteristics of students who are included vs. excluded in student lists purchased from College Board?


Two mechanisms of racial and socioeconomic exclusion in student list products (Salazar, Jaquette, and Han, 2022):

  1. Who is included in the underlying database
  2. Structurally racist and classist search filters


Salazar, Jaquette, and Han (2022) categorizes College Board search filters into four buckets:

  • Geographic
  • Academic
  • Demographic
  • Student preferences


Drawing largely from the sociology of race, we develop expectations about which filters are associated with problematic exclusion


Geographic filters

Geographic search filters enable universities to target prospects based on where they live

  • e.g., state, zip code, CBSA, “geomarket,” geodemographic market segment)


Critical geography and whiteness as property Harris (1993); Salazar (2022)

  • Residential segregation a function of historic and contemporary laws/policies/practicies
  • Geographic filters are built on the back of racial segregation
  • Targeting prospects based on location (space) without considering history of segregation (place) reinforces race-based inequality


Expected results

  • Small geographic filters (e.g., zip code vs. metro) >> racial disparities because segregation granular
    • Targeting affluent communities >> racial exclusion because POC historically excluded
  • Filters that create new borders based on historic education data >> racial disparities because borders reflect historic disparities in educational opportunities
    • “Geographic education market” filters: geomarket, geodemographic segment
    • These filters increases the effects of historic place-based inequality
      • Discriminate between prospects based on previously unknown geographic borders

— .section

Methods

— .subsection

Data collection


Data collection overview

Data collection

  • Issued public records requests to all public universities in four states (CA, IL, MN, TX)
  • Target student list vendors: College Board, ACT
  • Data collection began February 2020, sought purchases from 2016-2020


For each purchased list, sought two pieces of data

  1. “Order summary” specifying search filter criteria (LINK)
  2. De-identified prospect-level student list (LINK)


Empirical research questions

  1. Which filter criteria were selected in student lists purchases?
  2. What are the characteristics of prospects included in student lists purchases?
  3. What is the relationship between student list filter criteria and the characteristics of purchased prospects?

Summary of data received

State # received order summary # no order summary # received list # no list # received both # did not receive both
CA 9 23 13 19 9 23
IL 9 3 9 3 8 4
TX 15 20 16 19 10 25

Orders and prospects purchased

— .subsection

Research design


Summary of orders and prospects

RQ1 RQ3 RQ2 RQ3
# orders total # orders with list # prospects total # prospects with order
830 414 3,663,257 2,549,085


Empirical research questions

  1. Which filter criteria were selected in student lists purchases?
  • Unit of analysis = order; 830 orders (by 14 universities)
  1. What are the characteristics of prospects included in student lists purchases?
  • unit of analysis = university-prospect; 3,663,257 prospects (by 14 universities)
  1. What is relationship between filter criteria and characteristics of purchased prospects?
  • Unit of analysis = order-prospect; 414 orders associated with 2,548,085 prospects


Case study research design because non-random sample

  • RQ1 and RQ2
    • Internal validity: are orders/prospects representative of behavior of 14 universities in sample?
    • External validity: cannot make inferences about population of public univs
  • RQ3
    • Ixternal validity: set of search criteria yield same prospects regardless of which univ purchases
    • Analyses focus on “deep dives” of conceptually important order combinations

— .section

Results

— .subsection

RQ1

— .subsubsection

Broad patterns

Filters used in order purchases

— .subsubsection

Academic filters

GPA filter used


SAT filter used


PSAT filter used

— .subsubsection

Geographic filters

State filter used by research universities, out-of-state


State filter used by research universities, in-state

— .subsubsection

Demographic filters

Race filter

— .subsubsection

Combination of filters

Filter combos used in order purchases

Research MA/doctoral
Filters Count Percent Filters Count Percent
HS grad class, GPA, SAT, PSAT, Rank, State, Race 39 10% HS grad class, GPA, SAT, Zip code 206 45%
HS grad class, PSAT, State 27 7% HS grad class, GPA, PSAT, Zip code 145 32%
HS grad class, GPA, PSAT, State, Race 20 5% HS grad class, SAT, State 31 7%
HS grad class, PSAT, State, Low SES 20 5% HS grad class, GPA, SAT, PSAT, Zip code 28 6%
HS grad class, GPA, PSAT, State 17 5% HS grad class, GPA, SAT, State 7 2%
HS grad class, GPA, SAT, State 16 4% HS grad class, SAT, Geomarket 6 1%
HS grad class, GPA, AP score, Geomarket 15 4% HS grad class, GPA, SAT, County 5 1%
HS grad class, GPA, SAT, PSAT, State, Segment, Gender 13 3% HS grad class, GPA, SAT, PSAT, County 4 1%
HS grad class, PSAT, Geomarket 12 3% HS grad class, GPA, PSAT, State 2 0%
HS grad class, SAT, State, Low SES, College size 11 3% HS grad class, SAT, Geomarket, College type 2 0%

— .subsection

RQ2


Characteristics of Prospects

Number of prospects by university type and location

— .subsubsection

Public research universities

Racial composition of prospects in lists purchased


Median household income of prospects in lists purchased


Locale of prospects in lists purchased

— .subsubsection

Public ma/doctoral universities

Racial composition of prospects in lists purchased


Median household income of prospects purchased


Locale of prospects in lists purchased

— .subsection

RQ3

— .subsubsection

Characteristics by filters

Prospect characteristics across individual filter criteria

Academic Geographic Demographic
All domestic GPA PSAT SAT HS rank AP score Zip code State Geomarket Segment CBSA Race Gender
Total 3,547,620 1,101,266 1,812,447 971,237 146,660 75,479 165,924 1,173,678 1,056,951 186,519 146,313 279,626 39,546
Location
% In-state 38 62 30 54 83 42 98 48 17 15 4 59 6
% Out-of-state 62 38 70 46 17 58 2 52 83 85 96 41 94
Race/ethnicity
% White 48 45 50 47 51 17 43 42 57 51 53 25 47
% Asian 16 15 17 15 10 7 13 18 13 27 28 5 38
% Black 5 7 4 7 8 17 8 5 4 3 2 11 1
% Latinx 21 24 19 22 23 46 27 24 16 11 8 46 6
% AI/AN 1 1 1 0 1 1 1 1 0 0 0 2 0
% NH/PI 0 0 0 0 0 1 0 0 0 0 0 0 0
% Multiracial 5 5 5 5 5 10 4 6 5 5 5 9 5
% Other 0 0 0 0 0 0 0 0 0 0 0 0 0
% No response 4 3 3 3 2 1 4 3 4 3 3 2 3
% Missing 0 0 1 0 0 0 1 1 1 0 0 0 0
Gender
% Male 34 19 37 18 0 3 46 24 48 6 0 11 0
% Female 36 23 40 20 1 15 54 27 52 9 0 12 33
% Other 0 0 0 0 0 0 0 0 0 0 0 0 0
% Missing 30 58 22 63 99 82 0 49 0 85 1 77 67
Household income
Median income $107K $105K $108K $105K $99K $90K $97K $105K $107K $130K $135K $94K $127K
Locale
% City 27 27 27 26 26 31 31 30 23 24 22 29 26
% Suburban 44 47 44 48 53 40 42 42 46 54 57 47 49
% Rural - Fringe 22 20 22 20 15 23 19 22 23 19 19 19 23
% Rural - Distant 6 6 5 6 6 5 7 6 6 2 1 6 2
% Rural - Remote 1 0 1 0 0 0 1 1 1 0 0 0 0
% Missing 0 0 0 0 0 0 0 0 0 0 0 0 0

— .subsubsection

Zip code & test score filters

Los Angeles prospects from top income decile zip codes

— .subsubsection

Geodemographic segment filters

Filter by neighborhood segments

2011 D+ Cluster SAT Math SAT CR Going Out of State Percent NonWhite Need Financial Aid Med Income
51 546.00 533.00 32% 30% 57% $95,432
52 480.00 470.00 30% 58% 71% $63,578
53 561.00 544.00 32% 50% 55% $92,581
54 458.00 443.00 25% 83% 76% $38,977
55 566.00 565.00 52% 24% 63% $71,576
56 420.00 411.00 29% 93% 66% $35,308
57 541.00 519.00 52% 47% 43% $67,394
58 533.00 489.00 28% 87% 69% $68,213
59 561.00 562.00 52% 24% 74% $54,750
60 589.00 590.00 63% 37% 36% $104,174
61 585.00 567.00 51% 30% 40% $123,858
62 596.00 595.00 67% 24% 72% $59,824
63 548.00 541.00 39% 23% 65% $69,347
64 466.00 466.00 48% 34% 29% $49,829
65 440.00 433.00 23% 93% 78% $45,081
66 499.00 492.00 20% 12% 76% $50,453
67 519.00 501.00 27% 53% 59% $60,960
68 552.00 558.00 52% 35% 65% $57,902
69 534.00 521.00 37% 19% 65% $88,100
70 613.00 598.00 65% 29% 61% $86,381
71 405.00 408.00 39% 97% 68% $42,661
72 399.00 397.00 31% 87% 47% $32,708
73 528.00 514.00 29% 42% 62% $90,849
74 433.00 435.00 29% 84% 79% $44,065
75 459.00 457.00 28% 85% 72% $50,421
76 514.00 509.00 27% 38% 64% $61,332
77 502.00 492.00 26% 18% 75% $62,372
78 594.00 578.00 56% 26% 39% $134,400
79 550.00 551.00 57% 32% 74% $40,909
80 534.00 527.00 39% 39% 65% $49,877
81 491.00 483.00 27% 57% 72% $63,030
82 496.00 491.00 29% 21% 75% $53,465
83 500.00 490.00 19% 26% 71% $49,335
Total 512.00 502.00 32% 43% 65% $70,231

Filter by high school segments

2011 D+ Cluster SAT Math SAT CR Going Out of State Percent NonWhite Need Financial Aid Med Income
51 462.00 457.00 14% 33% 68% $40,918
52 489.00 496.00 81% 99% 77% $64,730
53 471.00 484.00 28% 38% 62% $60,833
54 376.00 371.00 33% 96% 38% $38,146
55 489.00 481.00 39% 46% 44% $71,845
56 536.00 508.00 73% 43% 49% $63,967
57 434.00 435.00 29% 82% 79% $48,301
58 592.00 577.00 51% 27% 32% $104,509
59 499.00 489.00 19% 18% 74% $47,685
60 523.00 549.00 23% 30% 33% $70,175
61 485.00 370.00 33% 89% 9% $61,385
62 474.00 473.00 34% 92% 67% $55,515
63 440.00 427.00 28% 86% 72% $49,238
64 606.00 542.00 37% 89% 57% $81,911
65 515.00 503.00 28% 43% 65% $72,692
66 498.00 515.00 37% 37% 73% $60,272
67 526.00 546.00 48% 41% 69% $71,279
68 541.00 540.00 41% 26% 62% $79,260
69 390.00 395.00 36% 92% 74% $43,391
70 595.00 581.00 56% 33% 48% $105,721
71 400.00 412.00 57% 98% 80% $43,137
72 528.00 544.00 35% 25% 64% $70,018
73 451.00 438.00 24% 89% 76% $48,406
74 654.00 579.00 76% 80% 46% $59,089
75 514.00 502.00 31% 20% 71% $72,850
76 600.00 584.00 72% 50% 28% $90,265
77 595.00 508.00 64% 75% 39% $39,490
78 473.00 468.00 48% 43% 22% $56,703
79 594.00 585.00 61% 26% 71% $65,180
Total 514.00 502.00 32% 44% 65% $70,223

Segment filter prospects by metro


Segment filter prospects interactive map

— .subsubsection

Women in STEM

Women in STEM prospects by metro

— .subsubsection

Targeting URM students

Race and ethnicity variables, aggregated vs. alone


Purchased profiles for students of color by metro


Purchased profiles for students of color interactive map

— .section

Discussion


Data as capital, obfuscation, and policy research

Student list data derived from user-data of students laboring on platforms

  • Marx (1978): formula for economic capital is \(M - C - M'\)
    • money (\(M\)); commodities (\(C\))
  • Data as capital (Sadowski, 2019)
    • Data an input into production commodities (e.g., software predicting hospital staff needs)
    • Data are a commodity extracted from labor of people using digital platforms
  • College Board follows \(M - C - M'-C-M''\): Invest money (\(M\)) to develop tests (\(C\)); sold to households (\(M'\)) yielding student list data (\(C\)); sold to universities (\(M''\))
  • Emerging trend: wrap student list data within a software-as-service recruiting product


Obfuscation (Cottom, 2020; Pasquale, 2015)

  • Opacity of digital platforms is deliberate strategy to manage regulatory environments
  • Really hard to collect data about student list products or “student success” products


Policy

  • Policy should regulate products sold to schools, universities, and students
  • Developing regulations requires on a body of research
  • Education researchers must interrogate third-party products and vendors
    • Focus on structural inequality embedded in product design

— #references

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